SAMPLE SIZES AND POWERS FOR ANOM-TYPE GRAPHICAL METHODS - Maremanda, Pran Kumar;Gollapudi, Venkata Sita Rama Anjaneyulu
59,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 1-2 Wochen
payback
30 °P sammeln
  • Broschiertes Buch

Analysis of means (ANOM) is a graphical test of hypothesis of homogeneity of several normal population means. ANOM has been extended for variances, corrrelation coefficients, autocorrelation coefficients etc. Sample size determination and power analysis are immensely useful for investigators in managing the time and controlling the cost and error in testing of hypothesis. A best test would be one that needs low sample size and attains high power. The authors determined the sample sizes and analyzed powers for two ANOM-type graphical methods of testing the significant difference among several…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
Analysis of means (ANOM) is a graphical test of hypothesis of homogeneity of several normal population means. ANOM has been extended for variances, corrrelation coefficients, autocorrelation coefficients etc. Sample size determination and power analysis are immensely useful for investigators in managing the time and controlling the cost and error in testing of hypothesis. A best test would be one that needs low sample size and attains high power. The authors determined the sample sizes and analyzed powers for two ANOM-type graphical methods of testing the significant difference among several variances and another ANOM-type graphical method of testing the significant difference among several correlation coefficients in the case of equal sample sizes. Also, analyzed powers for an ANOM-type graphical method of testing the significant difference among several autocorrelation coefficients of several lags. The authors compared the sample sizes for fixed powers as well as powers for fixed sample sizes between the two ANOM-type graphical methods of testing the significant difference among several variances and concluded the best method between them in the case of equal sample sizes.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Autorenporträt
Dr. M. Pran Kumar is a Senior Faculty in Statistics. He is a Guest Faculty (WILP) in Birla Institute of Technology and Science, Pilani, Rajasthan, India. He has 29 years of work experience.Dr. G.V.S.R Anjaneyulu is a Professor in the Department of Statistics Acharya Nagarjuna University, Guntur, A.P., India. He has 34 years of work experience.